Standard dynamic resource optimization approaches, such as value function iteration, are challenged by problems involving complex uncertainty and a large state space. We extend a solution technique to address these limitations called approximate dynamic programming (ADP). ADP recently emerged in the macroeconomics literature and is novel to bioeconomics. We demonstrate ADP in solving a simple fishery management model under uncertainty to show: the mechanics of ADP in simplest form; the accuracy of ADP; the value of a nonparametric extension; and readily adaptable, non-specialized code. We then demonstrate ADP’s capacity to handle rich bioeconomic problems by solving the fishery management problem subject to four autocorrelated shock processes (governing economic returns and biological dynamics) which entails four sources of stochasticity and five continuous state variables. We find that accounting for multiple autocorrelation has important impacts on harvest policy and generates gains that depend crucially on the structure of harvest cost.

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前进：解决动态资源管理问题的基于仿真的方法

标准的动态资源优化方法，例如价值函数迭代，受到涉及复杂不确定性和大状态空间的问题的挑战。我们扩展了一种解决方案技术来解决这些称为近似动态规划 (ADP) 的限制。ADP 最近出现在宏观经济学文献中，对生物经济学来说是新的。我们演示了 ADP 在解决不确定性下的简单渔业管理模型中的问题，以展示： 最简单形式的 ADP 机制；ADP的准确性；非参数扩展的值；和易于适应的非专业代码。然后，我们通过解决受四个自相关冲击过程（控制经济回报和生物动力学）影响的渔业管理问题来证明 ADP 处理丰富的生物经济问题的能力，这需要四个随机性来源和五个连续状态变量。我们发现，考虑多重自相关对收获政策有重要影响，并产生关键取决于收获成本结构的收益。